Novel targets in rectal cancer by considering lncRNA–miRNA–mRNA network in response to Lactobacillus acidophilus consumption: a randomized clinical trial

We aimed to explore the lncRNA–miR–mRNA network in response to Lactobacillus acidophilus (L. acidophilus) consumption in rectal cancer patients. The candidate miRs were first taken from the GEO and TCGA databases. We constructed the lncRNA–miR–mRNA network using the high-throughput sequencing data. At last, we created a heatmap based on the experimental data to show the possible correlation of the selected targets. The expression levels of selected targets were measured in the samples of 107 rectal cancer patients undergoing placebo and probiotic consumption and 10 noncancerous subjects using Real-Time PCR. Our analysis revealed a group of differentially expressed 12 miRs and 11 lncRNAs, and 12 genes in rectal cancer patients. A significant expression increase of the selected tumor suppressor miRs, lncRNAs, and genes and a substantial expression decrease of the selected oncomiRs, onco-lncRNAs, and oncogenes were obtained after the probiotic consumption compared to the placebo group. There is a strong correlation between some network components, including miR-133b and IGF1 gene, miR-548ac and MSH2 gene, and miR-21 and SMAD4 gene. In rectal cancer patients, L. acidophilus consumption was associated with improved expression of the lncRNA–miR–mRNA network, which may provide novel monitoring and therapeutic approaches.

The expression of onco-and tumor suppressor lncRNAs in pre-and post-intervention. The expression levels of the onco-lncRNAs, including CCAT1, LOC152578, UCA1, CRNDE, PVT1, MALAT1, XLOC_000303, XLOC_006844, BCAR4, and HOTAIR, were significantly increased in the rectal cancer patients compared to the control group. Their expression levels were significantly decreased following the probiotic consumption ( Fig. 6A-J) (P < 0.05). Unlike CCAT1, LOC152578, and XLOC_006844, the expression levels of the other onco-lncRNAs did not exhibit significant changes after the placebo consumption. Nevertheless, the expression levels of the onco-lncRNAs were meaningfully lower in the probiotic users than the placebo group (P < 0.05).
The expression level of tumor suppressor lncRNA, including LincRNA-P21, was significantly decreased in rectal cancer patients compared to the control group. It was dramatically increased following the probiotic consumption and was considerably higher in the probiotic users compared to the placebo group ( The expression of selected onco-and tumor suppressor miRs in pre-and post-intervention. The expression levels of oncomiRs, including miR-21, miR-20a, miR-20b, miR-424, miR-1244, miR-135b, and miR-224, were significantly increased in the rectal cancer patients compared to the control group. Their expression levels were considerably decreased following the probiotic consumption (P < 0.05) (Fig. 7A-G).  www.nature.com/scientificreports/ www.nature.com/scientificreports/ Unlike miR-1244, miR-378a, and miR-224, the other oncomiRs exhibited no significant changes after the placebo consumption. Notably, the expression levels of the oncomiRs were meaningfully lower in the probiotic group than the placebo (P < 0.05).
Our results showed that the expression levels of all selected tumor suppressor miRs, including miR-548ac, miR-378a, miR-34a, miR-601, and miR-133b, were significantly decreased in the rectal cancer patients compared to the control group, which were considerably increased following the probiotic consumption (P < 0.05) ( Fig. 7H-L). Except for miR-378a and miR-34a, the levels of the other tumor suppressor miRs exhibited no significant changes after the placebo consumption. Notwithstanding, the expression levels of the tumorsuppressor miRs were meaningfully higher in the probiotic than the placebo users (P < 0.05).
The expression of selected onco-and tumor suppressor genes in pre-and post-intervention. The expression levels of oncogenes, including SMAD4, IGF1, GRB10, BCL2, CCND1, MYC, AKT3, TGFBR2, and CYCS, were significantly increased in the rectal cancer patients compared to the control group. Their expression levels were considerably decreased following the probiotic consumption (P < 0.05) ( Fig. 8A-I). Except for BCL2, SMAD4, MYC, and TGFBR2, the other oncogenes revealed no significant changes after the placebo consumption ( Fig. 8A-I). Nonetheless, the expression levels of the oncogenes were significantly lower in the probiotic group than the placebo (P < 0.05).
Moreover, our results showed that the tumor suppressor genes' expression levels, including MAPK11, TP53, and MSH2, were significantly decreased in the rectal cancer patients compared to the control group, which were considerably increased following the probiotic consumption (P < 0.05) ( Fig. 8J-L). The placebo consumption did not significantly impact the selected tumor suppressor genes ( Fig. 8J-L). Interestingly, the expression levels of the selected tumor suppressor genes were meaningfully higher in the probiotic group than the placebo group (P < 0.05).
Correlation of the lncRNA-miR-mRNA network. To further understand the role of differential expression of ceRNAs in rectal cancer, we performed a correlation analysis between lncRNAs, miRs, and mRNAs. Consequently, 11 lncRNAs, 12 miRs, and 12 mRNAs constituted a direct regulatory relationship of the lncRNA-miRNA-mRNA network (Fig. 9). Accordingly, there was a strong correlation between some network components, including miR-133b and IGF1 gene, miR-548ac and MSH2 gene, and miR-21 and SMAD4 gene. Likewise, we created a heat map of the expression of the selected lncRNAs, miRs, and mRNAs using CIMminer (https:// disco ver. nci. nih. gov/ cimmi ner/ home. do) (Fig. 10). www.nature.com/scientificreports/

Discussion
This study investigated the effects of L. acidophilus consumption on the expression of lncRNAs, miRs, and mRNAs in patients with non-metastatic rectal cancer. Based on a biphasic methodology, we constructed a network of the lncRNA-miR-mRNA using bioinformatics analyses. 11 lncRNAs, 12 miRs, and 12 genes have displayed significant differential expressions in cancerous tissues compared to noncancerous tissues. Besides, our experimental results have shown that the L. acidophilus consumption was associated with an expressional improvement of candidate lncRNAs, miRs, and mRNAs compared to the placebo group. Some genes can play a role in tumorigenesis and the progression of colorectal cancer. Accordingly, TGF-βR2 is a trans-membrane serine-threonine kinase and is the only known receptor complex for TGF-β to be phosphorylated. It, in turn, may phosphorylate downstream proteins, including the SMAD, PI3K, p38MAPK, PKA, and RhoA, leading to inhibiting cell proliferation, inducing apoptosis, terminating differentiation, and maintaining genetic stability. Furthermore, CCND1 expression was significantly related to lymph nodes and distant metastases. There was a significant statistical correlation between the CCND1 gene and high stages in colorectal cancer 15 . Here, we observed that the probiotic consumers had a lower expression level of CCND1 than the placebo group. Similar to our results, tumor suppressor genes such as the MAPK can also be up-regulated in probiotic consumption 16 . Our analysis showed a considerable interaction between the candidate DEGs and miRs such as miR-21, miR-20a, and miR-34a. The miR-21 overexpression is associated with a non-complete response to preoperative chemo-radiotherapy in patients with rectal adenocarcinomas 17,18 . Moreover, it was reported that c-Myc up-regulates the miR-17 and down-regulates the angiogenesis inhibitors. Dews et al. showed that the overexpression of miR-20a is associated with reduced TGF-βR2 protein levels in colon cells. They represented that the TGF-βR2 can be a direct target of miR-17/20a. This inhibition would deactivate the downstream mediators such as SMAD and thrombospondin type I, which can be associated with inhibition of angiogenesis in tumor cells 19 . In this setting, the miR-34 family is a transcriptional target of the p53, directly suppressing a set of  Although there is a strong link between changes in the intestinal microbiome and rectal cancer, the potential mediators of these relationships are unclear. Accordingly, our bioinformatics study analyzed the lncRNA-miR-mRNA network of the essence in rectal carcinogenesis. In this setting, several lncRNAs can target one miR by inhibiting its expression through various mechanisms. According to our results, HOTAIR, as a lncRNA, can negatively regulate the expression of miR-203a-3p, miR-545, and miR-218, leading to EGFR and VOPP1 regulation, which can be found to be related to chemotherapy resistance in rectal cancer 21 . LncRNAs can bind to miR-34a and disrupt the regulation of miRs and target genes, including GAPLINC and SNHG7, which may increase, migrate, and invade rectal cancer cells 22,23 . While lncRNAs have the necessary pathological properties for appropriate biomarkers, their extraction and measurement are limited. As a result, although several studies reported differences in the expression of new lncRNA markers in human plasma and serum, others have difficulty replicating 21 . However, there is no doubt that lncRNAs and miRs are essential players in cancer pathology and can be a significant regulator of CRC's biology in cell cultures, animal models, and human  www.nature.com/scientificreports/ samples. Future systematic and integrated analysis of different RNA molecules with potential cross-discussion may greatly help unravel the complex mechanisms of tumorgenesis and treatment of rectal cancer. The experiments and trials regarding the beneficial effects of probiotics in the cancer region showed promising results. Commensal Lactobacillus species (such as L. acidophilus) are normal inhabitants of the natural microbiota 24 . Importantly, probiotics have been shown to reduce colon cancer incidence in animal models 10,25 . Oral administration of L. acidophilus has effectively reduced colon carcinoma growth, suggesting that its consumption was associated with suppressed tumor growth 10,26 . In an animal model, Chen et  Accordingly, probiotics could alleviate the complication of CRC patients who underwent surgery or chemoradiation therapy 30 . Kim et al. found that radiation causes significant changes in the microbiome abundance and diversity 31 that can influence the effectiveness of the anti-cancer treatments. Moreover, the immune microenvironment may modulate radiosensitivity related to radiation injury. Current evidence supports the use of probiotics as adjunctive therapy. They might have beneficial effects on some aspects of toxicity related to radiotherapy. www.nature.com/scientificreports/ It seems that probiotics could be safely administered even in neutropenia 32 . A meta-analysis study showed that probiotics could reduce the incidence of diarrhea induced by radiotherapy and have beneficial effects in preventing radiation-induced diarrhea, especially for grade ≥ 2 or 3 diarrhea. They may be a safe, promising therapeutic alternative for cancer patients suffering radiotherapy-induced diarrhea 33 . In addition, probiotics have been shown to reduce tumor recurrence rates and protect the intestinal mucosa's physical and biological barrier functions. They can improve the integrity of the intestinal epithelial layer and increase resistance to pathogenic colonization 34 . They can also produce a fasting-induced adipose factor, a gut radioprotector 35 . Moreover, two probiotic strains, including Lactobacillus fermentum and Lactobacillus salivarius, could re-establish miR-155 and miR-223 expression, preserve the mucosal barrier function, and relieve the DSS-induced colitis 36 . Tan et al. performed a comprehensive analysis of the lncRNA-miR-mRNA regulatory network for microbiota-mediated colorectal cancer 37 . They showed that probiotics could regulate lncRNAs' expression levels by competitively binding to the corresponding miRs and mRNAs, called ceRNA regulatory network 38 . These researchers identified a set of microbiota-mediated biomarkers and constructed ceRNA networks in CRC. Accordingly, 75 DELs, 8 DEMs, and 9 DEGs in the probiotic-related ceRNA network were obtained. They exhibited that the probiotics could inhibit the oncogenes' expression, including miR-153 and miR-429, and promote the tumor suppressors' expression, including miR-140 and miR-132 37 . They also showed that four lncRNAs from the microbiota-mediated ceRNA network, including LINC00355, KCNQ1OT1, LINC00491, and HOTAIR, were found to be associated with poor overall survival. These results could indicate a potential mechanism where probiotics can regulate immune system functions in CRC.
Here, we have demonstrated that the administration of probiotics could improve the molecular profile of rectal cancer patients. This novel effect yielded that probiotics could play more fundamental roles in CRC management and co-administration with chemoradiation therapies to reduce complications and increase their efficacy. Likewise, similar outcomes were pursued by an unpublished study (NCT03072641) aiming to determine if probiotics could alleviate the cancer-associated gut microbiota and epigenetic alterations in CRC. Moreover, Zaharudinn et al. reported that the probiotics containing six viable microorganisms could reduce the post-surgical pro-inflammatory cytokines such as TNF-α, IL-6, IL-10, and IL-12 in CRC patients 39 . However, comprehensive research should be assumed better to understand the clinical values of probiotics in colorectal cancer. Therefore, it will be with much more clinical efficacy if the clinicians and researchers apply mechanism-oriented and population-specific approaches when dealing with probiotics. www.nature.com/scientificreports/

Conclusion
During radiotherapy, L. acidophilus consumption in rectal cancer patients for 13 weeks could reduce oncogenic lncRNAs, miRs, and mRNAs and simultaneously increase tumor-suppressor lncRNAs, miRs, and mRNAs. Our results suggest interactions among lncRNAs, miRs, and genes may mediate host-microbial interactions in rectal cancer and can be an explicit goal for developing treatment strategies. Moreover, promising therapeutic approaches for activating endogenous miR expression to mediate lncRNA silencing mediated by target miRs have been proposed, although more works need to be evaluated.

Materials and methods
Study setting. This study is part of an ongoing randomized clinical trial registered in the Iranian randomized control trial (NO: IRCT2014092118745N3). The study is a randomized, double-blind, and single-center conducted on 107 new cases (55 males and 52 females) with non-metastatic rectal cancer at Emam-Khomeini Hospital, Tehran, Iran, which entered into the study on 08-11-2014 (Fig. 5). All participants were informed of the current research objectives, study protocol, and informed consent to participate in the study.

Randomization, allocation, and interventions.
Randomization was performed on block randomization. The block randomization was performed based on blocks of 2, and the computer program performed it. Sealed envelopes with the treatment codes were stored in the same department. The patients were blinded using an identical capsule to those given to the intervention group as a placebo. Besides, the caregiver and the laboratory staff were all blinded to the patient's medical documents. Similarly, the statistician who performed the statistical analyses was also blinded to the grouping codes assigned in the dataset. The patients in the probiotic group received probiotic capsules (500 mg) (10 9 CFU) for 13 weeks, taking the tablets three times a day. The www.nature.com/scientificreports/  www.nature.com/scientificreports/ subjects in the placebo group received placebo capsules with the same shape, color, and smell as the probiotic group's protocol.
Primary outcomes. We measured and compared the expression levels of candidate lncRNAs (Table 5), miRs (Table 1), and mRNAs (Table 4) before (baseline) and after three months of the intervention in the probiotic and placebo groups.
Predicted target genes and lncRNAs of candidate miRs. Construction and analysis of the lncRNA-miR-mRNA network. The lncRNA-miR-mRNA network was constructed and visualized using Cytoscape software based on the ceRNA theory. Here, the nodes and edges were used to represent extensive biological data. Intuitively, each node represents a biological molecule, and the edges stand for the interactions between nodes and the node degrees. The edges were calculated to exploit the hub nodes that possess essential biological functions 46 . A network analysis was performed using Cytoscape software to explore the structure and feature of the lncRNA-miR-mRNA competing triplets. Topological parameters of standard centrality measures in a network, including DC, BC, and CC, were assessed. The DC is defined as the number of links incident upon a node. The BC for each node is calculated as the number Figure 11. A flowchart diagram for used bioinformatics analysis in the present study. www.nature.com/scientificreports/ of these shortest paths that pass through the node. The CC is the length of the shortest paths between the node and other nodes in the network.
Correlation of lncRNA-miR-mRNA network. We constructed a heat map based on our experimental data to show the possible correlation of the selected lncRNAs, miRs, and mRNAs. The absolute value of the correlation coefficient (equal to or more than 0.5) represented a significant correlation. We matched interactions of miRs according to the miR-code database (http:// www. mirco de. org/) by the differentially expressed lncRNA and miRs. Moreover, the target genes of miRs were created using the mentioned databases. At last, a ceRNA network between lncRNAs, miRs, and mRNAs was constructed using Cytoscape 3.1 software.
The analysis of the GO term and KEGG pathways by FunRich software. The FunRich (http:// www. funri ch. org) is software for functional gene classification. The GO and KEGG (map05210) 47 enrichment analyses of the DEGs were executed through the FunRich software.
Sample collection. Before and after the intervention, 10 ml blood samples were obtained from all the subjects using disposable vacutainer blood collection tubes. The blood was centrifuged at 3000g for 5 min, and peripheral blood mononuclear cells (PBMCs) were then isolated by the Ficoll-Hypaque (Amersham  48,49 . The expression of U6 and B-actin was used to normalize miRs, lncRNAs, and genes as the Internal Reference Gene. The list of primers has shown in Table 9. The qRT-PCR reactions were performed using an ABI StepOne plus System (Applied Biosystems; Thermo Fisher Scientific, Inc). The expression level of the genes was calculated using the − ΔCT method. ΔCT was calculated by subtracting the CT values of U6 and B-actin from the CT values of the targets 50 . The expression data generated from our study samples have been available as a supplementary information file (Supplementary Information).
Statistical analysis. The statistical analysis was carried out using GraphPad Prism 6.01 (https:// www. graph pad. com) software. The one-sample K-S test was used to evaluate the normality of the data. The t-test and ANOVA were used to analyze the data in two and multiple groups. The descriptive analysis for quantitative data was performed using mean ± SD. The same analysis was performed for qualitative data by representing the frequencies and regarded percentages. We constructed a correlation network of the selected lncRNA-miR-mRNA using the R Core Team (2019), R Foundation for Statistical Computing, Vienna, Austria. URL https:// www.Rproje ct. org. The statistical significance was defined as P < 0.05. www.nature.com/scientificreports/

Data availability
The data that support the findings of this study are available from the corresponding author, AMA, upon reasonable request.   MAPK11  CTG AAC AAC ATC GTC AAG TGCC  CAT AGC CGG TCA TCT CCT CG   AKT3  TGA AGT GGC ACA CAC TCT AACT  CCG CTC TCT CGA CAA ATG GA   SMAD4  ACG AAC GAG TTG TAT CAC CTGG  TGC ACG ATT ACT TGG TGG ATG   CCND1  CAA TGA CCC CGC ACG ATT TC  CAT GGA GGG CGG ATT GGA A   CYCS  CTT TGG GCG GAA GAC AGG TC  TTA TTG GCG GCT GTG TAA GAG   IGF1  TCG ACA TCC GCA ACG ACT ATC  CCA GGG CGT AGT TGT AGA AGAG   TGFBR2  GCT TTG CTG AGG TCT ATA AGGC  GGT ACT CCT GTA GGT TGC CCT   BCL2  TCG CCC TGT GGA TGA CTG A  CAG AGA CAG CCA GGA GAA ATCA   GRB10  CTC GTG GCA ATG GAT TTT TCTG  TCA CTG TAC TTA GGG TAG AAGGG   MYC  CAC ACC CAC AAT TCA GGA AGAG  GAC GTG CTA CAA GGT GGC A   TP53  ACT TGT CGC TCT TGA AGC TAC  GAT GCG GAG AAT CTT TGG AACA   MSH2  GAT CAA TCC CCA GTC TGT TGTT  CCA AAA TCC TGG TAA CAG  GGG AGA AGC AGG ATT TAG GATG   XLOC_000303  CCC TGT TGA TTG ACT TGT CTTG  CTT CTC TTG CTG TCT CCT ACC   LinRNAP21  TCT TGT GGT GGT AAA GAC A  CCT CAA TGC AGG CAT ACA CAT   U6  ATG CAG TCG AGT TTC CCA CAT  CCA TGA TCA CGA AGG TGG TTT